Title

Performance of radial-basis function networks for direction of arrival estimation with antenna arrays

Keywords

Antenna arrys; Direction of arrival estimation

Abstract

The problem of direction of arrival (DOA) estimation of mobile users using linear antenna arrays is addressed. To reduce the computational complexity of superresolution algorithms, e.g. multiple signal classification (MUSIC), the DOA problem is approached as a mapping which can be modeled using a suitable artificial neural network trained with input output pairs. This paper discusses the application of a three-layer radial-basis function neural network (RBFNN), which can learn multiple source-direction findings of a six-element array. The network weights are modified using the normalized cumulative delta rule. The performance of this network is compared to that of the MUSIC algorithm for both uncorrelated and correlated signals. It is also shown that the RBFNN substantially reduced the CPU time for the DOA estimation computations. © 1997 IEEE.

Publication Date

12-1-1997

Publication Title

IEEE Transactions on Antennas and Propagation

Volume

45

Issue

11

Number of Pages

1611-1617

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/8.650072

Socpus ID

0031271507 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0031271507

This document is currently not available here.

Share

COinS